{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Purpose 😇"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a brief notebook to demonstrate how you can save a learner once your model has been trained for later inference (to generate predictions) or to continue training it when new samples become available. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Install and load libraries 📚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Running command git clone -q https://github.com/timeseriesAI/tsai.git /tmp/pip-req-build-uoszu6pa\n"
     ]
    }
   ],
   "source": [
    "# **************** UNCOMMENT AND RUN THIS CELL IF YOU NEED TO INSTALL/ UPGRADE TSAI ****************\n",
    "stable = False # Set to True for latest pip version or False for main branch in GitHub\n",
    "!pip install {\"tsai -U\" if stable else \"git+https://github.com/timeseriesAI/tsai.git\"} >> /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "os             : Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n",
      "python         : 3.7.13\n",
      "tsai           : 0.3.2\n",
      "fastai         : 2.6.0\n",
      "fastcore       : 1.4.2\n",
      "torch          : 1.10.0+cu111\n",
      "device         : 1 gpu (['Tesla T4'])\n",
      "cpu cores      : 2\n",
      "RAM            : 12.69 GB\n",
      "GPU memory     : [14.75] GB\n"
     ]
    }
   ],
   "source": [
    "from tsai.all import *\n",
    "my_setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train model 🏃‍♀️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y, splits = get_UCR_data('LSST', split_data=False)\n",
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = TSStandardize(by_sample=True)\n",
    "dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms, path='/data/')\n",
    "learn = ts_learner(dls, InceptionTimePlus, metrics=accuracy, cbs=[ShowGraph()])\n",
    "learn.fit_one_cycle(10, 1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference: learn.export and load_learner 🚚"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have finished training, you can export the model for inference using the `export` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.export('exported.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you need to generate predictions you just fo this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(TensorBase([[1.2905e-03, 1.5663e-04, 8.7045e-02,  ..., 4.4778e-02, 6.6327e-05,\n",
       "          2.5620e-03],\n",
       "         [5.1583e-05, 5.3598e-07, 4.0279e-02,  ..., 1.6094e-04, 8.1969e-08,\n",
       "          3.4691e-03],\n",
       "         [2.1993e-02, 1.3445e-05, 1.8652e-01,  ..., 1.3897e-01, 2.7750e-05,\n",
       "          3.2740e-02],\n",
       "         ...,\n",
       "         [8.9264e-04, 5.0546e-05, 2.5908e-01,  ..., 3.3627e-03, 7.0707e-05,\n",
       "          6.4735e-01],\n",
       "         [1.9133e-02, 3.7902e-06, 2.5041e-01,  ..., 6.3816e-01, 5.1981e-05,\n",
       "          1.9923e-02],\n",
       "         [1.4637e-04, 1.6800e-06, 5.7432e-01,  ..., 7.2760e-04, 8.9442e-07,\n",
       "          2.3213e-01]]),\n",
       " None,\n",
       " array(['6', '6', '62', ..., '95', '90', '42'], dtype='<U2'))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_X, *_ = get_UCR_data('LSST', split_data=False)\n",
    "learn1 = load_learner('/data/exported.pth', cpu=False) # set cpu to True or False depending on your environment\n",
    "preds, _, decoded_preds = learn1.get_X_preds(new_X)\n",
    "preds, _, decoded_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partial fit or fine tuning 🏋️‍♂️"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's another way to export the learner keeping the optimizer state in case we need to keep training the model on some new data. `save` will save the model and optimizer state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('/data/models/test.pth')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we have some new data, we'll create a new learner as before and load the model weights and optimizer state. Then we can fit the model on some more epochs or fine tune it. You can try both methods and see which one works best in your case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incremental learning: 🙇🏽‍♀️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.501133</td>\n",
       "      <td>0.890488</td>\n",
       "      <td>0.717356</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not enough values to plot a chart\n"
     ]
    }
   ],
   "source": [
    "new_X, new_y, new_splits = get_UCR_data('LSST', split_data=False)\n",
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = TSStandardize(by_sample=True)\n",
    "dls2 = get_ts_dls(new_X, new_y, splits=new_splits, tfms=tfms, batch_tfms=batch_tfms, path='/data/')\n",
    "learn2 = ts_learner(dls2, InceptionTimePlus, metrics=accuracy, cbs=[ShowGraph()])\n",
    "learn2 = learn2.load('/data/models/test', device=device)\n",
    "learn2.fit_one_cycle(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tuning 📻"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.485616</td>\n",
       "      <td>0.887381</td>\n",
       "      <td>0.710057</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not enough values to plot a chart\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.477844</td>\n",
       "      <td>0.890763</td>\n",
       "      <td>0.710462</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not enough values to plot a chart\n"
     ]
    }
   ],
   "source": [
    "new_X, new_y, new_splits = get_UCR_data('LSST', split_data=False)\n",
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = TSStandardize(by_sample=True)\n",
    "dls3 = get_ts_dls(new_X, new_y, splits=new_splits, tfms=tfms, batch_tfms=batch_tfms, path='/data/')\n",
    "learn3 = ts_learner(dls3, InceptionTimePlus, metrics=accuracy, cbs=[ShowGraph()])\n",
    "learn3 = learn3.load('/data/models/test', device=device)\n",
    "learn3.fine_tune(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
